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The Painter's Feature Selection for Gene Expression Data

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4 Author(s)
Daniele Apiletti ; Politecnico di Torino, Italy. phone: 0039 011 090 7084; fax: 0039 011 090 7099; daniele.apiletti@polito.it ; Elena Baralis ; Giulia Bruno ; Alessandro Fiori

Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multi- category method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007